Calculation of soil electrical conductivity using a genetic algorithm

Calixto, Wesley P.; Neto, Luciano Martins; Wu, Marcel; Kliemann, Huberto José; Castro, S.; Yamanaka, Kazushi

doi:10.1016/j.compag.2009.12.002

Cited by 17 publications

(12 citation statements)

References 8 publications

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“…By mimicking natural biological evolution, the GA which is a heuristic search algorithm provides the best value for a function [51]. A GA feature selection process starts with an initial random population consisting of individuals.…”

Section: Selection Of Auxiliary Variables Using Genetic Algorithms (Ga)mentioning

confidence: 99%

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Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

et al. 2020

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Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin’s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.

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Section: Selection Of Auxiliary Variables Using Genetic Algorithms (Ga)mentioning

confidence: 99%

“…In this study, we decided to implement the GA, one of the most advanced algorithms for feature selection [46]. GA can manage the datasets with many features and do not need specific knowledge about the problem parallelizing easily in computer clusters [46,51,52].…”

Section: Introductionmentioning

confidence: 99%

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

et al. 2020

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“…The soil electrical conductivity [41] of each experimental group decreased compared with controls but was only significantly different in B2, with a reduction of 40.35 units (31.35%) (Figure 2C). This might be because the levels of some bases are too high and some bases are missing, given that woody peat can absorb bases from the soil.…”

Section: Effects On Soil Electrical Conductivitymentioning

confidence: 93%

Effects of the Rapid Construction of a High-Quality Plough Layer Based on Woody Peat in a Newly Reclaimed Cultivated Land Area

Zhang

Zhao

et al. 2021

Agriculture

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To implement the balance system of cultivated land in occupation and supplement and to adhere to the principle of “supplement the occupied cultivated land of high quality with the one bearing same quality”, in the thesis, a field experiment was conducted to study the effects of woody peat on soil physical, chemical, and biological properties of the plough layer and its crop yield. Furthermore, the correlation between soil indexes and crop yield under the best fertilization mode through the addition of the natural material of woody peat instead of lengthy cultivation of the plough layer to rapidly construct a high-quality plough layer and solve the practical problem that the natural endowment of newly reclaimed cultivated land is far less than the occupied high-quality cultivated land was explored. The land remediation project of Fuping County, Hebei Province, was taken as the experimental area, and the five most representative and effective datasets were selected and studied. The results demonstrated that the addition of woody peat and rotten straw could reduce soil particle size and bulk density and alleviate soil viscosity and acidification. An increase in soil organic matter, soil microbial biomass carbon (MBC), alkali-hydrolyzable nitrogen, available phosphorus, and available potassium and a decrease in the heavy metal content were also observed. The results indicated that the application of woody peat achieved the expected effect of the rapid construction of a high-quality plough layer. The best mode of fertilization was A2, which provided a good reference for the rapid construction of a high-quality plough layer in the future. The analysis of the correlation between soil indexes and crop yield illustrated that the organic matter content, soil available nutrients, and crop yield had a significant positive correlation; the organic matter content and soil available nutrients showed the same tendency, which suggests that soil organic matter content and soil fertility level are closely related and that soil fertility plays a decisive role in crop yield under the same external conditions. Woody peat exerted an eminent influence on the organic matter content and soil available nutrients to determine the change in crop yield, which provides a reliable basis for future research on land improvement projects to increase crop yield.

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“…The current-voltage four-electrode method is a contact measurement method. Two electrodes are used to conduct the driving current to soil and the other two electrodes are used to detect the output signals (voltage) to calculate soil EC [11] . It has a simple structure and less affected by electromagnetic noises [12] .…”

Section: Introduction mentioning

confidence: 99%

Measurement of soil electrical conductivity based on direct digital synthesizer (DDS) and digital oscilloscope

Pei

Meng

et al. 2019

International Journal of Agricultural and Biological Engineering

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A soil electrical conductivity (EC) measurement system based on direct digital synthesizer (DDS) and digital oscilloscope was developed. The system took the "current-voltage four-electrode method" as the design principal and adopted a six-pin structure of the probe, two center pins to measure the soil EC in shallow layer, two outside pins to measure the soil EC in deep layer, and two middle pins for inputting the driving current. A signal generating circuit using DDS technology was adopted to generate sine signals, which was connected with the two middle pins. A digital oscilloscope was used to record and store the two soil output signals with noises in microseconds, which were from the two center pins and two outside pins, respectively. Then a digital bandpass filter was used to filter the soil output signals recorded by the digital oscilloscope. Compared with the traditional analog filter circuit, the digital filter could filter out the noises of all frequency except for the frequency of the excitation source. It could improve the effect of filtering and the accuracy of the soil EC measurement system. The DDS circuit could provide more stable sine signals with larger amplitudes. The use of digital oscilloscope enables us to analyze the soil output signals in microseconds and measure the soil EC more accurately. The new soil EC measurement system based on DDS and digital oscilloscope can provide a new effective tool for soil sensing in precision agriculture.

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Calculation of soil electrical conductivity using a genetic algorithm

Cited by 17 publications

References 8 publications

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Effects of the Rapid Construction of a High-Quality Plough Layer Based on Woody Peat in a Newly Reclaimed Cultivated Land Area

Measurement of soil electrical conductivity based on direct digital synthesizer (DDS) and digital oscilloscope

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